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Poster Session 3, 1:15 PM - 2:00 PM: Room 163 [C11]

Presenter: Devina Gera

Faculty Sponsor: Greg Gregory

School: UMass Amherst

Research Area: Computer Science

ABSTRACT

Sequential decision problems arise in many real world domains where models must act on evolving, noisy data while accounting for feedback between predictions and actions. These settings introduce challenges including distribution shift, partial observability, and complex adaptation dynamics that are not fully captured by static predictive modeling. This thesis investigates how machine learning systems behave in non stationary sequential decision environments, with emphasis on stability, generalization across regimes, and sensitivity to uncertainty in model outputs. Financial markets serve as a representative high noise testbed in which signals are heterogeneous, environments evolve over time, and decisions influence future observations. The project explores how different learning approaches, including sequence modeling, probabilistic inference, and decision focused learning, affect downstream behavior in such environments. Rather than optimizing domain specific performance, the work focuses on understanding the learning dynamics that govern robustness, adaptation, and consistency of decisions over time. Through empirical analysis across simulated and real time-series settings, the thesis aims to identify methodological principles that support reliable decision making under shifting conditions. The broader goal is to contribute insight into how machine learning systems can be designed to operate effectively in complex sequential environments characterized by uncertainty and change.